high performance computing on graphics processing units: hgpu.org

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Parallel Spherical Harmonic Transforms on heterogeneous architectures (GPUs/multi-core CPUs)

Mikolaj Szydlarski, Pierre Esterie, Joel Falcou, Laura Grigori, Radek Stompor

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Tags: Algorithms, Astrophysics, Cluster computing, Computational Physics, Cosmology, Cosmology and Extragalactic Astrophysics, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 480, Tesla S1070

June 6, 2012 by hgpu

GPU-accelerated simulation of colloidal suspensions with direct hydrodynamic interactions

Michael Kopp, Felix Hofling

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Tags: Algorithms, Computational Physics, CUDA, Molecular dynamics, nVidia, Package, Physics, Tesla C2050, Tesla S1070

May 30, 2012 by hgpu

Combustion Simulations Using Graphic Processing Units

Mingjie Wang

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Tags: Algorithms, CUDA, nVidia, Physics, Tesla S1070

May 19, 2012 by hgpu

GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics

Shuji Suzuki, Takashi Ishida, Ken Kurokawa, Yutaka Akiyama

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Tags: Algorithms, Biology, CUDA, Databases, Next-Generation sequencing, nVidia, Package, Tesla S1070

May 10, 2012 by hgpu

Solving incompressible two-phase flows on multi-GPU clusters

Peter Zaspel, Michael Griebel

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Tags: CUDA, Finite difference, Fluid dynamics, GPU cluster, Navier-Stokes equations, nVidia, Tesla C2050, Tesla S1070

April 16, 2012 by hgpu

Scheduling Tasks over Multicore machines enhanced with Accelerators: a Runtime System’s Perspective

Cedric Augonnet

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Tags: Cell processor, Computer science, CUDA, Databases, nVidia, nVidia Quadro FX 4600, nVidia Quadro FX 5800, Task scheduling, Tesla C2050, Tesla M2070, Tesla S1070, Thesis

March 29, 2012 by hgpu

A computing origami: Optimized code generation for emerging parallel platforms

Hagiescu Miriste Andrei Mihai

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Tags: Code generation, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 8800 GTX, Optimization, Tesla S1070, Thesis

March 29, 2012 by hgpu

Shallow water simulations on multiple GPUs

Martin Lilleeng Saetra, Andre Rigland Brodtkorb

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 480, Tesla C2050, Tesla S1070

March 21, 2012 by hgpu

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Dimitar Lukarski

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FEM, Finite difference, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla M1060, Tesla S1070, Thesis

March 16, 2012 by hgpu

Dynamic Scheduling for Work Agglomeration on Heterogeneous Clusters

Jonathan Lifflander, G. Carl Evans, Anshu Arya, Laxmikant V. Kale

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Tags: Computer science, CUDA, Heterogeneous systems, N-body simulation, nVidia, Particle simulation, Tesla S1070

March 9, 2012 by hgpu

Scalability of Self-organizing Maps on a GPU cluster using OpenCL and CUDA

Sabine McConnell, Robert Sturgeon, Gregory Henry, Andrew Mayne, Richard Hurley

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Tags: Algorithms, Computer science, CUDA, Data mining, GPU cluster, nVidia, nVidia GeForce GT 220, OpenCL, Self-organizing map, Tesla S1070

February 11, 2012 by hgpu

Enabling Traceability in MDE to Improve Performance of GPU Applications

Antonio Wendell De Oliveira Rodrigues, Vincent Aranega, Anne Etien, Frederic Guyomarc'H, Jean-Luc Dekeyser

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Tags: Algorithms, Computer science, nVidia, OpenCL, Performance, Programming techniques, Tesla S1070

February 7, 2012 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Parallel Spherical Harmonic Transforms on heterogeneous architectures (GPUs/multi-core CPUs)

GPU-accelerated simulation of colloidal suspensions with direct hydrodynamic interactions

Combustion Simulations Using Graphic Processing Units

GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics

Solving incompressible two-phase flows on multi-GPU clusters

Scheduling Tasks over Multicore machines enhanced with Accelerators: a Runtime System’s Perspective

A computing origami: Optimized code generation for emerging parallel platforms

Shallow water simulations on multiple GPUs

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Dynamic Scheduling for Work Agglomeration on Heterogeneous Clusters

Scalability of Self-organizing Maps on a GPU cluster using OpenCL and CUDA

Enabling Traceability in MDE to Improve Performance of GPU Applications

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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